What is YOLO-NAS?

YOLO-NAS is an object detection model developed by Deci that achieves SOTA performances compared to YOLOv5, v7, and v8.

About the model

Here is an overview of the

YOLO-NAS

model:

Date of Release May 03, 2023
Model Type Object Detection
Architecture YOLO
Framework Used PyTorch
Annotation Format YOLOv5 PyTorch TXT
Stars on GitHub 2400+

YOLO-NAS is an object detection model developed by Deci that achieves SOTA performances compared to YOLOv5, v7, and v8.

YOLO-NAS performance (Source: Deci YOLO-NAS README).

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Model Performance

Model mAP Latency (ms)
YOLO-NAS S 47.5 3.21
YOLO-NAS M 51.55 5.85
YOLO-NAS L 52.22 7.87
YOLO-NAS S INT-8 47.03 2.36
YOLO-NAS M INT-8 51.0 3.78
YOLO-NAS L INT-8 52.1 4.78

mAP numbers in table reported for Coco 2017 Val dataset and latency benchmarked for 640x640 images on Nvidia T4 GPU.

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Deploy YOLO-NAS to production

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YOLO-NAS Annotation Format

YOLO-NAS

uses the

uses the

YOLOv5 PyTorch TXT

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.

Convert data between formats

Label data automatically with YOLO-NAS

You can automatically label a dataset using

YOLO-NAS

with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use

YOLO-NAS

to train a computer vision model.

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